Load Data

url="C:/Users/rohit/Desktop/CS Courses/DPA/Project/Crimes_-_2001_to_present.csv"
testurl="C:/Users/rohit/Desktop/CS Courses/DPA/Project/Crimes_2017_Test.csv"
df <- data.frame(read.csv(file=url, header=TRUE, sep=","))
testdf <- data.frame(read.csv(file=testurl, header=TRUE, sep=","))
sprintf("Number of Rows in Dataframe: %s", format(nrow(df),big.mark = ","))
[1] "Number of Rows in Dataframe: 268,482"
df <- subset(df, select = -c(Case.Number, Block, IUCR,Beat,District,Ward,Community.Area,X.Coordinate,Y.Coordinate,FBI.Code,Updated.On))
testdf <- subset(testdf, select = -c(Case.Number, Block, IUCR,Beat,District,Ward,Community.Area,X.Coordinate,Y.Coordinate,FBI.Code,Updated.On))
head(df)
write.csv(df,"format.csv", row.names = FALSE)

Data Processing

library(lubridate)
# Create a variable count with value 1
df$Count <- 1
testdf$Count <- 1
# Convert Date from factor to date
df$Date <- mdy_hms(df$Date)
testdf$Date <- mdy_hms(testdf$Date)
# Extract hour from Date
df$Hour <- substring(df$Date, 12,13)
testdf$Hour <- substring(testdf$Date, 12,13)
# Drop time from Date
df$Date <- as.Date(df$Date, format="%m/%d/%Y")
testdf$Date <- as.Date(testdf$Date, format="%m/%d/%Y")
write.csv(df,"format.csv", row.names = FALSE)
aa = table(rowSums(is.na(df)))
aa

     0      2 
266532   1950 
#Remove all NA
df <- df[complete.cases(df),]
testdf <- testdf[complete.cases(testdf),]
head(df)

###Visualizations

library(ggplot2)
library(ggrepel)
df_loc <- sort(table(df$Location.Description),decreasing = TRUE)
df_loc <- data.frame(df_loc[df_loc > 10000])
colnames(df_loc) <- c("Location", "Frequency")
df_loc$Percentage <- df_loc$Frequency / sum(df_loc$Frequency)
df_loc
lp<-ggplot(df_loc, aes(x=Location, y=Frequency, fill=Location)) + geom_bar(stat="identity") + 
  theme(axis.text.x=element_blank()) + geom_text_repel(data=df_loc, aes(label=Location))
lp

library(ggplot2)
library(ggrepel)
df_category <- sort(table(df$Primary.Type),decreasing = TRUE)
df_category <- data.frame(df_category[df_category > 10000])
colnames(df_category) <- c("Category", "Frequency")
df_category$Percentage <- df_category$Frequency / sum(df_category$Frequency)
df_category
bp<-ggplot(df_category, aes(x=Category, y=Frequency, fill=Category)) + geom_bar(stat="identity") + 
  theme(axis.text.x=element_blank()) + geom_text_repel(data=df_category, aes(label=Category))
bp

bp<-ggplot(df_category, aes(x="", y=Percentage, fill=Category)) + geom_bar(stat="identity") 
pie <- bp + coord_polar("y") 
pie

###Integrating With Map

library(ggmap)
#library(dmm)
register_google(key = 'AIzaSyAtlORy7YG5gJ9GA52crQgHJqmz_2xYeEM')
#get the map of LA
Chi_map <- qmap(location = "Chicago", zoom = 12)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Chicago&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Chicago&key=xxx
Chi_map

#unfactor variable
#df$Latitude <- unfactor(df$Latitude)
#df$Longitude <- unfactor(df$Longitude)

####HeatMaps

library(dplyr)

Attaching package: <U+393C><U+3E31>dplyr<U+393C><U+3E32>

The following objects are masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:

    intersect, setdiff, union

The following objects are masked from <U+393C><U+3E31>package:stats<U+393C><U+3E32>:

    filter, lag

The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    intersect, setdiff, setequal, union
#select relevant variables
mapping <- df %>% select(Primary.Type, Longitude, Latitude) %>% filter(Primary.Type == 'THEFT') %>%
  na.omit() 
Chi_map + geom_density_2d(aes(x = Longitude, y = Latitude), data = mapping) +
  stat_density2d(data = mapping, 
    aes(x = Longitude, y = Latitude, fill = ..level.., alpha = ..level..), size = 0.01, 
    bins = 16, geom = "polygon") + scale_fill_gradient(low = "green", high = "red", 
    guide = FALSE) + scale_alpha(range = c(0, 0.3), guide = FALSE)

mapping <- df %>% select(Primary.Type, Longitude, Latitude) %>% filter(Primary.Type == 'MOTOR VEHICLE THEFT') %>%
  na.omit() 
Chi_map + geom_density_2d(aes(x = Longitude, y = Latitude), data = mapping) +
  stat_density2d(data = mapping, 
    aes(x = Longitude, y = Latitude, fill = ..level.., alpha = ..level..), size = 0.01, 
    bins = 16, geom = "polygon") + scale_fill_gradient(low = "green", high = "red", 
    guide = FALSE) + scale_alpha(range = c(0, 0.3), guide = FALSE)

###Predict Crime Spots

library(leaflet)
data <- df[1:10000,] # display the first 10,000 rows
data$popup <- paste("<b>Category: </b>", data$Category,
                    "<br>", "<b>Description: </b>", data$Primary.Type,
                    "<br>", "<b>Description: </b>", data$Description,
                    "<br>", "<b>Date: </b>", data$Date,
                    "<br>", "<b>Time: </b>", data$Hour,
                    "<br>", "<b>Arrest?: </b>", data$Arrest,
                    "<br>", "<b>Longitude: </b>", data$Longitude,
                    "<br>", "<b>Latitude: </b>", data$Latitude)
leaflet(data, width = "100%") %>% addTiles() %>%
  addTiles(group = "OSM (default)") %>%
  addProviderTiles(provider = "Esri.WorldStreetMap",group = "World StreetMap") %>%
  addProviderTiles(provider = "Esri.WorldImagery",group = "World Imagery") %>%
  # addProviderTiles(provider = "NASAGIBS.ViirsEarthAtNight2012",group = "Nighttime Imagery") %>%
  addMarkers(lng = ~Longitude, lat = ~Latitude, popup = data$popup, clusterOptions = markerClusterOptions()) %>%
  addLayersControl(
    baseGroups = c("OSM (default)","World StreetMap", "World Imagery"),
    options = layersControlOptions(collapsed = FALSE)
  )

###Clustering Analysis

library(fields)
package <U+393C><U+3E31>fields<U+393C><U+3E32> was built under R version 3.5.3Loading required package: spam
package <U+393C><U+3E31>spam<U+393C><U+3E32> was built under R version 3.5.3Loading required package: dotCall64
package <U+393C><U+3E31>dotCall64<U+393C><U+3E32> was built under R version 3.5.3Loading required package: grid
Spam version 2.4-0 (2019-11-01) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: <U+393C><U+3E31>spam<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:Matrix<U+393C><U+3E32>:

    det

The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    backsolve, forwardsolve

Loading required package: maps
package <U+393C><U+3E31>maps<U+393C><U+3E32> was built under R version 3.5.3See https://github.com/NCAR/Fields for
 an extensive vignette, other supplements and source code 
lon = df$Longitude[1:1000]
lat = df$Latitude[1:1000]
threshold.in.km <- 40
coors <- data.frame(lon,lat)
testlon = testdf$Longitude[1:600]
testlat = testdf$Latitude[1:600]
threshold.in.km <- 40
testcoors <- data.frame(testlon,testlat)
#distance matrix
dist.in.km.matrix <- rdist.earth(coors,miles = F,R=6371)
#clustering
fit <- hclust(as.dist(dist.in.km.matrix), method = "single")
clusters <- cutree(fit,h = threshold.in.km)
str(fit)
List of 7
 $ merge      : int [1:999, 1:2] -7 -11 -47 -99 -118 -132 -147 -927 -162 -621 ...
 $ height     : num [1:999] 0 0 0 0 0 0 0 0 0 0 ...
 $ order      : int [1:1000] 638 790 197 523 110 192 359 27 454 804 ...
 $ labels     : NULL
 $ method     : chr "single"
 $ call       : language hclust(d = as.dist(dist.in.km.matrix), method = "single")
 $ dist.method: NULL
 - attr(*, "class")= chr "hclust"
plot(lon, lat, col = clusters, pch = 20)

K-Menas Clustering - Cluster Analysis

#Theft <- df %>% select(Primary.Type, Longitude, Latitude) %>% filter(Primary.Type == 'THEFT') %>%
#  na.omit() 
#lon1 = Theft$Longitude[1:500]
#lat1 = Theft$Latitude[1:500]
#coors <- data.frame(lon1,lat1)
library(geosphere)
package <U+393C><U+3E31>geosphere<U+393C><U+3E32> was built under R version 3.5.3
library(ggmap)
library(NbClust)
geo.dist = function(df) {
  require(geosphere)
  d <- function(i,z){         # z[1:2] contain long, lat
    dist <- rep(0,nrow(z))
    dist[i:nrow(z)] <- distHaversine(z[i:nrow(z),1:2],z[i,1:2])
    return(dist)
  }
  dm <- do.call(cbind,lapply(1:nrow(df),d,df))
  return(as.dist(dm))
}
k<-list()
betweenSSByTotalSS <- list()
for(i in 1:6){
k[[i]] <- kmeans(coors,i)  
}
for(i in 1:6){
betweenSSByTotalSS[[i]]<-k[[i]]$betweenss/k[[i]]$totss 
}
plot(1:6,betweenSSByTotalSS,type="b",ylab="Between SS by Total SS",xlab="Clusters(k)")

km <- kmeans(geo.dist(coors),centers=2)
coors$Borough <- as.factor(km$cluster)
Chi_map <- get_map(location = "Chicago", zoom = 10)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Chicago&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Chicago&key=xxx
pqr = ggmap(Chi_map) + geom_point(aes(x = coors$lon, y = coors$lat, colour = as.factor(Borough)),data = coors)
plot(pqr)

testcoors <- testcoors %>% na.omit() 
testkm <- kmeans(geo.dist(testcoors),centers=2)
testcoors$Borough <- as.factor(testkm$cluster)
Chi_map <- get_map(location = "Chicago", zoom = 10)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Chicago&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Chicago&key=xxx
testpqr = ggmap(Chi_map) + geom_point(aes(x = testcoors$testlon, y = testcoors$testlat, colour = as.factor(Borough)),data = testcoors)
plot(testpqr)

#print(km['size'])
#print(km$totss)
print(km)
K-means clustering with 2 clusters of sizes 546, 454

Cluster means:
          1        2         3        4         5         6         7         8         9        10        11
1 17516.311 16231.58 15187.891  7002.47  9513.037 11607.319  5735.451  6603.373  7681.609 23617.841 16217.892
        12        13       14        15        16        17       18       19       20        21        22
1 18466.45 14937.765  6405.88  6687.519 19099.571 17007.228 20342.30  6404.69 28163.28 18739.499  9276.959
         23        24        25        26       27        28       29        30        31    32        33
1  7298.619  8270.427 15244.442  6454.109 29606.28  7208.502  5971.12  5823.067 18648.847  7534 15029.861
        34        35        36        37        38        39      40        41       42       43        44
1 10282.53  6641.103  5947.783 22610.153  6484.557 24977.291 14858.1 11607.499 21983.94  6404.69 15440.840
         45        46       47        48       49        50        51       52        53        54       55
1  7820.498 15644.391 10769.74  8178.882 15965.41 15109.099 13435.464 10918.05  6618.546  9018.031 10844.14
         56       57        58        59        60        61        62        63        64       65        66
1 19113.012 25554.14  6109.051  7486.084  8039.717  8010.979  5961.075 16397.410 24502.474 10769.74 13391.275
         67       68       69       70        71        72        73        74        75       76        77
1  5753.904 19036.92  7379.17  8142.97  8652.414 16217.892 12707.122  6229.526  8888.839 18842.58  5916.162
         78       79       80        81       82        83        84       85        86       87        88
1  8060.234  8106.22 19735.57  7420.036 13310.10 16298.441 24712.920  6915.95  6983.761 16995.65 13124.296
         89        90        91       92        93        94       95        96        97        98        99
1  7055.236  6533.162 19650.957 10083.99  7552.252  7015.627 13731.02 18844.859 18931.809  7116.728  7342.925
        100       101       102     103       104       105       106       107       108       109      110
1 23590.316 18712.835  7326.925  7800.8  7763.822  7625.878 19333.129  7270.552 12548.889 14546.372 28099.33
        111       112       113       114       115       116       117       118       119       120
1 16512.943 15547.166  7323.097 20633.596  7696.778  7845.244  6542.535  7175.095 12537.601  6061.056
        121       122       123       124       125       126       127      128      129       130       131
1  7123.789 24620.197  8191.611 15887.361  6247.373 19834.254  7981.411 10630.08 10988.89 24780.559 19629.833
        132       133      134      135      136       137       138       139      140       141       142
1  7311.753  5859.156 24825.01 12837.07 26059.07  8424.052 15445.933  6801.334 13168.74  6909.783 22013.829
        143      144       145       146       147       148       149       150       151       152       153
1  8861.942  6257.72  8301.355 17735.915  6490.817  7461.011 14559.892 25243.723  7036.604  6699.625  7742.975
       154       155       156       157      158       159       160       161      162       163       164
1 27551.71 15663.278  7402.287  5839.752 19449.48  8568.535  8606.161  7692.138 19103.60  7290.951  8374.405
        165       166       167       168       169       170       171       172       173       174
1 19841.373  6757.799  5896.514  9102.602  7023.696 12291.846 22458.250 14721.582  5846.725 21076.294
        175      176       177       178       179      180      181       182       183       184       185
1  7601.643 25667.10 20437.257 23532.970 12387.456 12542.85 10244.16  6447.484 17079.736  7971.073 15009.028
        186       187       188       189       190       191      192      193       194       195       196
1  6324.023  6069.986  7921.867 15949.520  8024.132  5934.088 27545.52 12411.99 23036.966 13870.495  5923.257
       197      198       199       200       201       202      203       204       205      206       207
1 16665.62  6958.41  6942.164  6486.357  6179.774  5742.045 13051.99  5898.161 20086.227 11080.41 18548.361
        208       209       210       211       212      213       214       215       216      217      218
1  8060.934  8735.073  6577.627  6962.632  5935.961 20942.87  8982.201  6500.421 21706.106 23613.89 13674.79
       219       220       221       222       223       224       225       226       227       228       229
1 14509.42  7310.834  7175.095  7311.753  8298.964  8170.149 25607.513  6282.352  7815.457  6292.308 16504.264
        230       231       232       233       234      235       236       237       238       239       240
1  8022.873 19843.279 23850.329 23912.745  7565.847 17777.98 18214.455  7407.751  8148.723  7565.847  7997.723
        241       242       243       244       245       246       247       248      249       250      251
1  7363.943 22864.533  7895.131  5887.592 15959.171  6938.159 13559.917  8404.047  6372.80  7750.137 26396.27
        252      253       254       255       256       257       258       259       260      261      262
1  6070.021 15314.41  7142.036  6854.954  8062.719  7843.606  7546.521  7896.491  7473.215 11791.91 19370.65
        263       264       265       266       267       268       269       270       271       272
1  5736.246  7141.142  8300.903  8142.535  7516.612  6292.308 22690.013 19213.718 17232.801  8012.256
        273       274      275       276       277       278       279       280      281      282       283
1  6955.461  5765.834 15583.83  7754.413 19336.500  7692.322 11797.863  7310.309  6287.96  7217.01  8481.047
        284      285       286       287       288       289       290      291       292      293       294
1 23498.460 16338.03  7961.522  7343.942 15623.004 12713.071  7536.808 10323.25  6337.616  7065.07 10415.103
        295       296       297       298       299       300       301       302       303      304       305
1 26108.107  6802.657  8332.226 17097.962 17097.962  9697.048  7167.163 15751.037 18503.328 19116.76 25150.525
       306       307       308       309       310       311       312      313       314      315       316
1  9561.47  5812.013 25638.420  7317.591  7498.203 15999.503  6317.417  5795.20 22176.633 12779.86  7826.331
        317       318       319       320       321       322       323       324       325       326      327
1 13622.451  6509.531 16447.303 20914.407 16812.839  7644.913  8140.331  7816.953 19480.749 16817.426  6893.96
        328       329       330       331       332       333       334       335       336       337      338
1  7844.056  6517.292  8488.552  7913.134  7543.829 17202.623  6253.863 19781.024  8811.422  6405.197 10051.67
        339       340      341       342      343       344       345       346       347       348       349
1 13931.700  6295.239 18372.65 15824.828 11791.91 17930.178 19257.994 13593.396  7050.768  8205.566 18320.824
        350      351      352      353       354       355       356      357      358      359      360
1  6092.697 13874.52  8359.70 28087.37 18993.799 13253.005 20007.551  8777.08  7633.72 29495.80 19556.48
        361       362      363       364       365       366      367       368       369       370       371
1 16784.772 23326.666 12895.65  8123.366  7559.473  7732.267 16141.47  5895.846 17805.135  7129.902 15023.477
        372      373       374      375       376       377       378       379       380       381       382
1  7371.098  7058.88  9971.966  7971.11  6195.518  7297.699 22691.213  7992.016  8619.005  8619.005  8671.188
        383       384      385       386       387       388       389       390       391      392       393
1 17951.139 20973.496  6297.41  6612.906 15393.390  6053.175 21100.685  5942.593  9781.797 13874.52 16604.820
        394       395      396       397       398      399      400       401       402       403       404
1  7283.873 15021.760 10681.50 11125.469 18525.065 18404.70  9818.38  6079.799  7986.352  7986.352 20536.342
        405       406       407       408       409       410       411       412       413      414       415
1  7216.415  7750.137  6163.307 15196.972 14206.418  8313.781 14580.049 17953.746  6184.024 18606.36 24602.934
       416       417       418       419       420       421       422       423       424       425       426
1 26072.62  7662.079  7908.579  6525.268 22266.622  8231.851  7691.308 12228.350 21742.283  9392.476 20070.609
        427       428       429       430       431       432       433       434       435       436
1  9473.079  7378.764  9832.253 16347.341  8514.116  8040.082  7782.712  6525.268  9075.526 25539.422
        437       438       439       440       441       442       443       444       445       446      447
1  7826.992 20715.809  8655.108  7496.597 14578.958  6239.143  7916.509 14737.480  7895.514 17092.946 18372.65
        448       449       450       451       452      453      454       455       456       457       458
1 25569.080 11693.758 21153.487  7795.208 19908.451 26109.50 29794.96  7027.046  6649.857 19511.456  7628.715
        459       460       461       462       463      464      465       466       467       468      469
1  7628.715  5922.583 21924.075 14277.214 16935.693 18827.49 10193.29  6863.965 18449.434  7345.002 14835.79
        470       471       472       473       474       475       476       477       478       479
1  9806.895  8142.472 17632.932  7159.279 17171.053 22833.899 25164.526 23594.008  7019.588  7666.288
        480       481       482       483      484       485       486       487       488      489      490
1 13386.946 16335.533  7761.092 18555.782 28402.33 22921.189 18582.959 23378.959  7548.553 12878.55 10305.90
        491       492       493       494       495       496       497       498       499       500
1  8551.875 15961.180  7795.208 18427.277 17342.827 20335.653 22747.763  7157.009 22502.171 20418.809
        501       502       503       504       505       506       507       508       509      510       511
1 12191.141 14638.648 22111.239  7156.196  6685.335  7498.843  5746.296  8293.998  8596.691 11475.53  6169.534
        512      513      514       515       516       517       518       519       520       521       522
1 18557.878 19103.60  7497.56  8071.721 16374.378  7007.281  6981.507  9069.543 24284.217 21479.843  7905.091
       523       524       525       526       527       528       529      530       531       532     533
1 15796.21 11446.980 21236.222 26694.119  7896.713  7582.436 19231.665 14652.44  9772.382  7748.407 13658.1
        534       535       536       537       538      539       540       541       542       543       544
1  8195.256  7455.612 17041.803  6018.978 21681.242 18985.45 17939.983  6941.139 14578.958 11435.470 23930.430
       545       546      547       548       549       550       551       552       553       554       555
1 10362.36  7146.057 27352.86  7980.664 15837.453 21797.526 18801.461  7825.457  7691.308  7143.214  6923.566
        556       557       558       559       560       561       562       563      564       565      566
1  8645.399  7150.803 13802.378 18781.159  7184.432 16809.931  5743.607  7750.137 19489.87  7157.009 12171.07
       567       568       569      570       571       572      573       574       575       576      577
1 10528.85  7717.605  6110.936 15547.07 11363.480  7081.253 25069.02  8858.035  6695.966  7632.191  6085.67
        578       579       580       581       582       583       584      585       586      587       588
1  7001.948 17821.310  8240.454 15968.548  6608.002 24928.474 21437.636 14808.96 14392.738  6284.63  7388.006
        589       590       591       592       593       594       595       596       597       598
1  7567.932  6150.365 14061.946 19684.669  6259.236 12468.783 21606.806  7539.317 20676.829 19826.310
        599       600       601       602      603       604       605       606       607       608       609
1  7159.564 19957.591 18721.227 17002.852  7015.93  7895.514  7864.472  6829.177  9366.046  6989.284 18854.776
        610      611       612       613      614       615       616       617       618       619       620
1 22218.931 12059.62 14623.343  7157.009 18827.49 23957.960 14132.805 20020.326  5823.165 15312.391  7813.747
       621       622       623       624       625       626      627       628      629       630       631
1 19103.60 19846.985 17363.013 11883.406 12335.217 16512.098 20249.91 15154.998  7546.42 24112.036 12472.230
        632       633       634       635       636       637      638      639      640      641       642
1  8442.627 18770.850 21165.121  7596.557 21585.217  6439.903 30656.67 10327.45  8056.32 13093.82  6092.578
        643      644       645       646      647       648       649       650       651      652       653
1  6212.818 10648.84 22960.770 18263.304  8113.15 13938.467 12807.353  7004.615 11883.406  7884.64  6665.086
        654       655       656       657       658       659       660       661      662       663      664
1 21599.145 16275.438  7740.971  6590.603 16596.883 17305.201  6322.031 19594.395 21498.24  6918.383 13929.22
       665       666       667       668       669       670      671       672       673       674       675
1  9058.65  6380.729 26716.067  6885.164 19311.925  7635.409 10550.86 22607.253  7286.226 14680.250  7938.704
        676       677       678       679       680       681       682       683       684       685
1 18545.313  7376.895  9505.696 20670.759  8000.118  9671.917  6512.042 20544.935  7339.215 25938.951
        686       687       688       689       690       691       692       693       694       695
1 18916.997  5739.668 24148.609 10114.184  6270.281  8444.772 15014.853 20241.985  6986.577  9350.308
        696       697       698       699       700      701       702       703       704       705       706
1  6370.314  5778.851  7415.756 15951.153 23707.796  6657.28 22841.683  6042.712  7577.917  7559.824  7282.556
       707       708       709       710       711      712       713      714       715       716       717
1  7849.29  7234.512 20313.452 16904.903 18052.809 20946.05  7402.473  8595.82 15443.250  6134.323 12448.530
        718      719       720       721       722       723       724       725       726       727       728
1  6016.094 12349.97 15538.208 18763.215 22902.550  7235.707 20376.123 22537.997  7670.666 21699.782 17031.021
        729       730       731       732       733       734       735       736       737       738
1  7987.058 18497.651  9033.558  7081.253 16760.180  7530.462 22169.475  9181.943  8319.013  6803.437
        739       740       741       742      743       744       745       746      747      748       749
1  7750.137 23301.162 20141.764  8044.106 10092.12 20073.460  7594.073  5735.451 18446.38 21711.83 22853.889
        750      751       752      753       754       755       756      757      758       759       760
1  5765.257  7706.59  6865.483 11261.51 21624.197  7048.932  6066.367 19116.76  6935.36  9671.917 15440.276
        761      762      763       764       765       766       767       768       769      770       771
1  8725.237 13093.82  8021.45 21390.784  8614.105  8956.365 20848.203  5906.325  8401.386 14100.53 17128.673
        772       773       774       775       776       777       778      779       780       781       782
1  6463.571 18578.152 14064.721  7383.713  7736.566 12885.414  6730.118 10746.04 20060.649 12393.736  7461.579
        783      784       785       786      787       788       789      790       791      792      793
1  8232.442 18195.12  6270.883  7471.342  7462.31  8671.805  7150.006 31599.93  8122.057 11405.18 18491.47
        794       795       796       797       798       799       800       801       802       803      804
1  6080.537 20238.483 12800.389 10505.166  9744.564 21519.266 13309.212  6089.509 22566.136 14125.118 30068.71
        805       806       807       808       809       810       811       812       813       814
1 15813.636 16360.419 20590.218 21579.710  7299.075  7952.828 16427.697 13487.874  6217.515 16283.170
        815       816       817       818       819      820       821       822       823       824       825
1 17526.429 11036.419 18552.040  8346.816 13575.827 23854.46  6000.504  6342.692 14002.499  6946.358  7459.623
        826       827      828       829       830       831       832       833       834       835       836
1 12047.956  8721.242 14509.42 12047.956 10013.559  7896.491 15312.692  6816.668 14083.484 22157.634 20532.218
        837      838       839       840      841       842       843       844       845       846       847
1  6169.666 29802.68 17103.741 21452.413  7297.15 13889.851 23048.655 17375.504 24454.540  7441.771 19257.994
        848       849       850       851       852       853       854       855      856       857       858
1 14159.373 24497.248 20779.730 19502.940 12502.060 14660.828 19698.252 18619.456  6781.83  8403.617  7656.399
        859       860       861       862       863       864       865       866       867       868
1 22303.069 16338.041 12357.887 17465.504 18986.047 10126.011 19097.343  7610.896 18962.744  6967.569
        869       870       871       872       873       874       875       876       877       878
1  5945.824  7750.137 25585.253  5934.088 13280.960  6575.935  8081.039 24786.378 24170.378  7692.262
        879       880       881       882       883      884       885       886       887       888      889
1  7931.331 16755.935 20540.179 25339.857  7957.858 11086.07 26207.668 17540.342  8813.144  9207.459 26769.99
        890       891       892       893       894       895       896       897       898       899
1  7750.137  7676.634  6153.474 16303.767 14867.999  7692.473  7066.477  8947.304 16138.446 18183.375
        900       901       902       903       904       905       906       907       908       909
1  8452.655 14978.220  6625.605  7699.875  6148.773 23739.982  5964.229 17465.504 13818.694 24865.684
        910       911       912       913       914       915       916       917       918       919      920
1  7760.838 19534.806 14266.159 18621.176  6016.962  9261.599  7024.662 21556.588  7913.102 21902.262  7516.46
       921       922       923       924       925       926       927       928       929       930       931
1  6297.41 13818.694 24395.756  6490.817  8907.691 25863.132  6490.817 21414.853 25038.182 18641.973  6621.385
        932       933       934       935       936       937       938       939      940       941       942
1  6997.648  8345.479  5954.756  6669.551 13724.479 15035.347 23342.080 14281.905 10796.21 15312.692 16087.193
        943       944       945       946      947       948       949       950       951       952       953
1 16037.372  7164.585  7751.031 25462.586 25411.94  6017.871  5984.147  7342.925  6745.943  7750.137  5842.859
        954       955       956       957       958       959       960       961       962       963
1 20076.582  8232.011 21752.418 23206.571 23206.571  8123.366 22082.939 14078.702 14078.702  6703.129
        964       965       966       967       968       969       970       971       972       973      974
1 13542.709 15539.237 11638.259  6012.346 18419.292  6316.663  8799.242 15892.029  7035.613  7556.606 11510.11
       975      976       977       978      979       980       981       982       983       984      985
1 17774.55  6341.14 14284.284 18177.317 11934.21 10909.067  6653.194  9282.774  6305.798  8603.725 10256.80
        986       987       988       989       990       991       992       993       994       995
1  8546.546 15543.331 23887.695  7252.058 15159.185 18716.542  9605.808 20854.357 21346.596  6621.651
        996      997      998       999     1000
1  6397.754 25516.34 10351.00 16261.720  5812.89
 [ reached getOption("max.print") -- omitted 1 row ]

Clustering vector:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20   21   22 
   2    1    2    1    1    2    1    1    1    2    2    2    2    1    1    2    2    2    1    2    2    1 
  23   24   25   26   27   28   29   30   31   32   33   34   35   36   37   38   39   40   41   42   43   44 
   1    1    2    1    2    1    1    1    2    1    2    1    1    1    2    1    2    1    2    2    1    2 
  45   46   47   48   49   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64   65   66 
   1    2    1    1    2    2    2    1    1    1    1    2    2    1    1    1    1    1    2    2    1    2 
  67   68   69   70   71   72   73   74   75   76   77   78   79   80   81   82   83   84   85   86   87   88 
   1    2    1    1    1    2    2    1    1    2    1    1    1    2    1    2    2    2    1    1    2    2 
  89   90   91   92   93   94   95   96   97   98   99  100  101  102  103  104  105  106  107  108  109  110 
   1    1    2    1    1    1    2    2    2    1    1    2    2    1    1    1    1    2    1    2    2    2 
 111  112  113  114  115  116  117  118  119  120  121  122  123  124  125  126  127  128  129  130  131  132 
   2    2    1    2    1    1    1    1    2    1    1    2    1    2    1    2    1    1    1    2    2    1 
 133  134  135  136  137  138  139  140  141  142  143  144  145  146  147  148  149  150  151  152  153  154 
   1    2    1    2    1    2    1    2    1    2    1    1    1    2    1    1    2    2    1    1    1    2 
 155  156  157  158  159  160  161  162  163  164  165  166  167  168  169  170  171  172  173  174  175  176 
   2    1    1    2    1    1    1    1    1    1    2    1    1    1    1    2    2    2    1    2    1    2 
 177  178  179  180  181  182  183  184  185  186  187  188  189  190  191  192  193  194  195  196  197  198 
   2    2    2    2    1    1    2    1    2    1    1    1    2    1    1    2    1    2    2    1    2    1 
 199  200  201  202  203  204  205  206  207  208  209  210  211  212  213  214  215  216  217  218  219  220 
   1    1    1    1    1    1    2    1    2    1    1    1    1    1    2    1    1    2    2    2    1    1 
 221  222  223  224  225  226  227  228  229  230  231  232  233  234  235  236  237  238  239  240  241  242 
   1    1    1    1    2    1    1    1    2    1    2    2    2    1    1    2    1    1    1    1    1    2 
 243  244  245  246  247  248  249  250  251  252  253  254  255  256  257  258  259  260  261  262  263  264 
   1    1    2    1    2    1    1    1    2    1    2    1    1    1    1    1    1    1    1    2    1    1 
 265  266  267  268  269  270  271  272  273  274  275  276  277  278  279  280  281  282  283  284  285  286 
   1    1    1    1    2    2    2    1    1    1    2    1    2    1    2    1    1    1    1    2    2    1 
 287  288  289  290  291  292  293  294  295  296  297  298  299  300  301  302  303  304  305  306  307  308 
   1    2    2    1    1    1    1    2    2    1    1    2    2    1    1    2    2    1    2    1    1    2 
 309  310  311  312  313  314  315  316  317  318  319  320  321  322  323  324  325  326  327  328  329  330 
   1    1    2    1    1    2    2    1    2    1    2    2    2    1    1    1    2    2    1    1    1    1 
 331  332  333  334  335  336  337  338  339  340  341  342  343  344  345  346  347  348  349  350  351  352 
   1    1    2    1    2    1    1    1    2    1    2    2    1    2    2    2    1    1    2    1    1    1 
 353  354  355  356  357  358  359  360  361  362  363  364  365  366  367  368  369  370  371  372  373  374 
   2    2    2    2    1    1    2    2    2    2    1    1    1    1    2    1    2    1    2    1    1    1 
 375  376  377  378  379  380  381  382  383  384  385  386  387  388  389  390  391  392  393  394  395  396 
   1    1    1    2    1    1    1    1    2    2    1    1    2    1    2    1    1    1    2    1    2    1 
 397  398  399  400  401  402  403  404  405  406  407  408  409  410  411  412  413  414  415  416  417  418 
   2    2    2    1    1    1    1    2    1    1    1    2    2    1    2    2    1    2    2    2    1    1 
 419  420  421  422  423  424  425  426  427  428  429  430  431  432  433  434  435  436  437  438  439  440 
   1    2    1    1    2    2    1    2    1    1    1    2    1    1    1    1    1    2    1    2    1    1 
 441  442  443  444  445  446  447  448  449  450  451  452  453  454  455  456  457  458  459  460  461  462 
   2    1    1    2    1    2    2    2    2    2    1    2    2    2    1    1    2    1    1    1    2    2 
 463  464  465  466  467  468  469  470  471  472  473  474  475  476  477  478  479  480  481  482  483  484 
   2    1    1    1    2    1    2    1    1    2    1    2    2    2    2    1    1    2    2    1    2    2 
 485  486  487  488  489  490  491  492  493  494  495  496  497  498  499  500  501  502  503  504  505  506 
   2    2    2    1    1    1    1    2    1    2    2    2    2    1    2    2    2    2    2    1    1    1 
 507  508  509  510  511  512  513  514  515  516  517  518  519  520  521  522  523  524  525  526  527  528 
   1    1    1    1    1    2    1    1    1    2    1    1    1    2    2    1    2    2    2    2    1    1 
 529  530  531  532  533  534  535  536  537  538  539  540  541  542  543  544  545  546  547  548  549  550 
   2    2    1    1    2    1    1    2    1    2    2    2    1    2    2    2    1    1    2    1    2    2 
 551  552  553  554  555  556  557  558  559  560  561  562  563  564  565  566  567  568  569  570  571  572 
   2    1    1    1    1    1    1    2    2    1    2    1    1    2    1    1    1    1    1    2    2    1 
 573  574  575  576  577  578  579  580  581  582  583  584  585  586  587  588  589  590  591  592  593  594 
   2    1    1    1    1    1    2    1    2    1    2    2    2    2    1    1    1    1    2    2    1    2 
 595  596  597  598  599  600  601  602  603  604  605  606  607  608  609  610  611  612  613  614  615  616 
   2    1    2    2    1    2    2    2    1    1    1    1    1    1    2    2    1    2    1    1    2    2 
 617  618  619  620  621  622  623  624  625  626  627  628  629  630  631  632  633  634  635  636  637  638 
   2    1    2    1    1    2    2    2    2    2    2    2    1    2    2    1    2    2    1    2    1    2 
 639  640  641  642  643  644  645  646  647  648  649  650  651  652  653  654  655  656  657  658  659  660 
   1    1    1    1    1    2    2    2    1    2    2    1    2    1    1    2    2    1    1    2    2    1 
 661  662  663  664  665  666  667  668  669  670  671  672  673  674  675  676  677  678  679  680  681  682 
   2    2    1    1    1    1    2    1    2    1    1    2    1    2    1    2    1    1    2    1    1    1 
 683  684  685  686  687  688  689  690  691  692  693  694  695  696  697  698  699  700  701  702  703  704 
   2    1    2    2    1    2    1    1    1    2    2    1    1    1    1    1    2    2    1    2    1    1 
 705  706  707  708  709  710  711  712  713  714  715  716  717  718  719  720  721  722  723  724  725  726 
   1    1    1    1    2    2    2    2    1    1    2    1    2    1    1    2    2    2    1    2    2    1 
 727  728  729  730  731  732  733  734  735  736  737  738  739  740  741  742  743  744  745  746  747  748 
   2    2    1    2    1    1    2    1    2    1    1    1    1    2    2    1    1    2    1    1    2    2 
 749  750  751  752  753  754  755  756  757  758  759  760  761  762  763  764  765  766  767  768  769  770 
   2    1    1    1    1    2    1    1    1    1    1    2    1    1    1    2    1    1    2    1    1    1 
 771  772  773  774  775  776  777  778  779  780  781  782  783  784  785  786  787  788  789  790  791  792 
   2    1    2    2    1    1    2    1    1    2    2    1    1    2    1    1    1    1    1    2    1    1 
 793  794  795  796  797  798  799  800  801  802  803  804  805  806  807  808  809  810  811  812  813  814 
   2    1    2    2    2    1    2    2    1    2    2    2    2    2    2    2    1    1    2    2    1    2 
 815  816  817  818  819  820  821  822  823  824  825  826  827  828  829  830  831  832  833  834  835  836 
   2    2    2    1    2    2    1    1    2    1    1    2    1    1    2    1    1    2    1    2    2    2 
 837  838  839  840  841  842  843  844  845  846  847  848  849  850  851  852  853  854  855  856  857  858 
   1    2    2    2    1    2    2    2    2    1    2    2    2    2    2    2    2    2    2    1    1    1 
 859  860  861  862  863  864  865  866  867  868  869  870  871  872  873  874  875  876  877  878  879  880 
   2    2    2    2    2    1    2    1    2    1    1    1    2    1    2    1    1    2    2    1    1    2 
 881  882  883  884  885  886  887  888  889  890  891  892  893  894  895  896  897  898  899  900  901  902 
   2    2    1    1    2    2    1    1    2    1    1    1    2    2    1    1    1    2    2    1    2    1 
 903  904  905  906  907  908  909  910  911  912  913  914  915  916  917  918  919  920  921  922  923  924 
   1    1    2    1    2    2    2    1    2    2    2    1    1    1    2    1    2    1    1    2    2    1 
 925  926  927  928  929  930  931  932  933  934  935  936  937  938  939  940  941  942  943  944  945  946 
   1    2    1    2    2    2    1    1    1    1    1    2    2    2    2    1    2    2    2    1    1    2 
 947  948  949  950  951  952  953  954  955  956  957  958  959  960  961  962  963  964  965  966  967  968 
   2    1    1    1    1    1    1    2    1    2    2    2    1    2    2    2    1    2    2    2    1    2 
 969  970  971  972  973  974  975  976  977  978  979  980  981  982  983  984  985  986  987  988  989  990 
   1    1    2    1    1    1    2    1    2    2    1    2    1    1    1    1    1    1    2    2    1    2 
 991  992  993  994  995  996  997  998  999 1000 
   2    1    2    2    1    1    2    1    2    1 

Within cluster sum of squares by cluster:
[1] 1.033989e+13 7.293884e+12
 (between_SS / total_SS =  65.9 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss" "betweenss"    "size"        
[8] "iter"         "ifault"      
#print(km$withinss)
---
title: "CS 571 Section 01"
author: "Rohit,Harika,Aayush"
date: "Nov 16, 2019"
output:
  html_document:
    df_print: paged
    toc: yes
  html_notebook:
    df_print: paged
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
  word_document:
    toc: yes
---

### Load Data
```{r}
url="C:/Users/rohit/Desktop/CS Courses/DPA/Project/Crimes_-_2001_to_present.csv"
testurl="C:/Users/rohit/Desktop/CS Courses/DPA/Project/Crimes_2017_Test.csv"
df <- data.frame(read.csv(file=url, header=TRUE, sep=","))
testdf <- data.frame(read.csv(file=testurl, header=TRUE, sep=","))
sprintf("Number of Rows in Dataframe: %s", format(nrow(df),big.mark = ","))
df <- subset(df, select = -c(Case.Number, Block, IUCR,Beat,District,Ward,Community.Area,X.Coordinate,Y.Coordinate,FBI.Code,Updated.On))
testdf <- subset(testdf, select = -c(Case.Number, Block, IUCR,Beat,District,Ward,Community.Area,X.Coordinate,Y.Coordinate,FBI.Code,Updated.On))
head(df)
write.csv(df,"format.csv", row.names = FALSE)
```

### Data Processing
```{r}
library(lubridate)
# Create a variable count with value 1
df$Count <- 1
testdf$Count <- 1
# Convert Date from factor to date
df$Date <- mdy_hms(df$Date)
testdf$Date <- mdy_hms(testdf$Date)
# Extract hour from Date
df$Hour <- substring(df$Date, 12,13)
testdf$Hour <- substring(testdf$Date, 12,13)
# Drop time from Date
df$Date <- as.Date(df$Date, format="%m/%d/%Y")
testdf$Date <- as.Date(testdf$Date, format="%m/%d/%Y")
write.csv(df,"format.csv", row.names = FALSE)
```

```{r}
aa = table(rowSums(is.na(df)))
aa
#Remove all NA
df <- df[complete.cases(df),]
testdf <- testdf[complete.cases(testdf),]
head(df)
```
###Visualizations
```{r}
library(ggplot2)
library(ggrepel)
df_loc <- sort(table(df$Location.Description),decreasing = TRUE)
df_loc <- data.frame(df_loc[df_loc > 10000])
colnames(df_loc) <- c("Location", "Frequency")
df_loc$Percentage <- df_loc$Frequency / sum(df_loc$Frequency)
df_loc
lp<-ggplot(df_loc, aes(x=Location, y=Frequency, fill=Location)) + geom_bar(stat="identity") + 
  theme(axis.text.x=element_blank()) + geom_text_repel(data=df_loc, aes(label=Location))
lp
```

```{r}
library(ggplot2)
library(ggrepel)
df_category <- sort(table(df$Primary.Type),decreasing = TRUE)
df_category <- data.frame(df_category[df_category > 10000])
colnames(df_category) <- c("Category", "Frequency")
df_category$Percentage <- df_category$Frequency / sum(df_category$Frequency)
df_category
bp<-ggplot(df_category, aes(x=Category, y=Frequency, fill=Category)) + geom_bar(stat="identity") + 
  theme(axis.text.x=element_blank()) + geom_text_repel(data=df_category, aes(label=Category))
bp
```

```{r}
bp<-ggplot(df_category, aes(x="", y=Percentage, fill=Category)) + geom_bar(stat="identity") 
pie <- bp + coord_polar("y") 
pie
```
###Integrating With Map
```{r}
library(ggmap)
#library(dmm)
register_google(key = 'AIzaSyAtlORy7YG5gJ9GA52crQgHJqmz_2xYeEM')
#get the map of LA
Chi_map <- qmap(location = "Chicago", zoom = 12)
Chi_map
#unfactor variable
#df$Latitude <- unfactor(df$Latitude)
#df$Longitude <- unfactor(df$Longitude)
```
####HeatMaps
```{r}
library(dplyr)
#select relevant variables
mapping <- df %>% select(Primary.Type, Longitude, Latitude) %>% filter(Primary.Type == 'THEFT') %>%
  na.omit() 
Chi_map + geom_density_2d(aes(x = Longitude, y = Latitude), data = mapping) +
  stat_density2d(data = mapping, 
    aes(x = Longitude, y = Latitude, fill = ..level.., alpha = ..level..), size = 0.01, 
    bins = 16, geom = "polygon") + scale_fill_gradient(low = "green", high = "red", 
    guide = FALSE) + scale_alpha(range = c(0, 0.3), guide = FALSE)

```

```{r}
mapping <- df %>% select(Primary.Type, Longitude, Latitude) %>% filter(Primary.Type == 'MOTOR VEHICLE THEFT') %>%
  na.omit() 

Chi_map + geom_density_2d(aes(x = Longitude, y = Latitude), data = mapping) +
  stat_density2d(data = mapping, 
    aes(x = Longitude, y = Latitude, fill = ..level.., alpha = ..level..), size = 0.01, 
    bins = 16, geom = "polygon") + scale_fill_gradient(low = "green", high = "red", 
    guide = FALSE) + scale_alpha(range = c(0, 0.3), guide = FALSE)

```
###Predict Crime Spots
```{r}
library(leaflet)

data <- df[1:10000,] # display the first 10,000 rows
data$popup <- paste("<b>Category: </b>", data$Category,
                    "<br>", "<b>Description: </b>", data$Primary.Type,
                    "<br>", "<b>Description: </b>", data$Description,
                    "<br>", "<b>Date: </b>", data$Date,
                    "<br>", "<b>Time: </b>", data$Hour,
                    "<br>", "<b>Arrest?: </b>", data$Arrest,
                    "<br>", "<b>Longitude: </b>", data$Longitude,
                    "<br>", "<b>Latitude: </b>", data$Latitude)

leaflet(data, width = "100%") %>% addTiles() %>%
  addTiles(group = "OSM (default)") %>%
  addProviderTiles(provider = "Esri.WorldStreetMap",group = "World StreetMap") %>%
  addProviderTiles(provider = "Esri.WorldImagery",group = "World Imagery") %>%
  # addProviderTiles(provider = "NASAGIBS.ViirsEarthAtNight2012",group = "Nighttime Imagery") %>%
  addMarkers(lng = ~Longitude, lat = ~Latitude, popup = data$popup, clusterOptions = markerClusterOptions()) %>%
  addLayersControl(
    baseGroups = c("OSM (default)","World StreetMap", "World Imagery"),
    options = layersControlOptions(collapsed = FALSE)
  )


```
###Clustering Analysis
```{r}
library(fields)
lon = df$Longitude[1:1000]
lat = df$Latitude[1:1000]
threshold.in.km <- 40
coors <- data.frame(lon,lat)

testlon = testdf$Longitude[1:600]
testlat = testdf$Latitude[1:600]
threshold.in.km <- 40
testcoors <- data.frame(testlon,testlat)

#distance matrix
dist.in.km.matrix <- rdist.earth(coors,miles = F,R=6371)

#clustering
fit <- hclust(as.dist(dist.in.km.matrix), method = "single")
clusters <- cutree(fit,h = threshold.in.km)
str(fit)
plot(lon, lat, col = clusters, pch = 20)

```
### K-Menas Clustering - Cluster Analysis
```{r}
#Theft <- df %>% select(Primary.Type, Longitude, Latitude) %>% filter(Primary.Type == 'THEFT') %>%
#  na.omit() 
#lon1 = Theft$Longitude[1:500]
#lat1 = Theft$Latitude[1:500]
#coors <- data.frame(lon1,lat1)

library(geosphere)
library(ggmap)
library(NbClust)
geo.dist = function(df) {
  require(geosphere)
  d <- function(i,z){         # z[1:2] contain long, lat
    dist <- rep(0,nrow(z))
    dist[i:nrow(z)] <- distHaversine(z[i:nrow(z),1:2],z[i,1:2])
    return(dist)
  }
  dm <- do.call(cbind,lapply(1:nrow(df),d,df))
  return(as.dist(dm))
}


k<-list()
betweenSSByTotalSS <- list()
for(i in 1:6){
k[[i]] <- kmeans(coors,i)  
}
for(i in 1:6){
betweenSSByTotalSS[[i]]<-k[[i]]$betweenss/k[[i]]$totss 
}
plot(1:6,betweenSSByTotalSS,type="b",ylab="Between SS by Total SS",xlab="Clusters(k)")


km <- kmeans(geo.dist(coors),centers=2)
coors$Borough <- as.factor(km$cluster)
Chi_map <- get_map(location = "Chicago", zoom = 10)
pqr = ggmap(Chi_map) + geom_point(aes(x = coors$lon, y = coors$lat, colour = as.factor(Borough)),data = coors)
plot(pqr)

testcoors <- testcoors %>% na.omit() 

testkm <- kmeans(geo.dist(testcoors),centers=2)
testcoors$Borough <- as.factor(testkm$cluster)
Chi_map <- get_map(location = "Chicago", zoom = 10)
testpqr = ggmap(Chi_map) + geom_point(aes(x = testcoors$testlon, y = testcoors$testlat, colour = as.factor(Borough)),data = testcoors)
plot(testpqr)

#print(km['size'])
#print(km$totss)
print(km)
#print(km$withinss)
```





